Github user jkbradley commented on the pull request: https://github.com/apache/spark/pull/2435#issuecomment-55972221 Each row is a single (random) dataset. The 2 different sets of result columns are for 2 different RF implementations: * (numTrees): This is from an earlier commit, after implementing RandomForest to train multiple trees at once. It does not include any code for feature subsampling. * (feature subsets): This is from this current PR's code, after implementing feature subsampling. These tests were to identify regressions in DecisionTree, so they are training 1 tree with all of the features (i.e., no feature subsampling). I have run other tests with numTrees=10 and with sqrt(numFeatures), and those indicate that multi-model training and feature subsets can speed up training for forests. (I'll update the description with this clarification.)
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